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MinVar: A rapid and versatile tool for HIV-1 drug resistance genotyping by deep sequencing


Huber, M; Metzner, K J; Geissberger, F D; Shah, C; Leemann, C; Klimkait, T; Böni, J; Trkola, A; Zagordi, O (2017). MinVar: A rapid and versatile tool for HIV-1 drug resistance genotyping by deep sequencing. Journal of Virological Methods, 240:7-13.

Abstract

Genotypic monitoring of drug-resistance mutations (DRMs) in HIV-1 infected individuals is strongly recommended to guide selection of the initial antiretroviral therapy (ART) and changes of drug regimens. Traditionally, mutations conferring drug resistance are detected by population sequencing of the reverse transcribed viral RNA encoding the HIV-1 enzymes target by ART, followed by manual analysis and interpretation of Sanger sequencing traces. This process is labor intensive, relies on subjective interpretation from the operator, and offers limited sensitivity as only mutations above 20% frequency can be reliably detected. Here we present MinVar, a pipeline for the analysis of deep sequencing data, which allows reliable and automated detection of DRMs down to 5%. We evaluated MinVar with data from amplicon sequencing of defined mixtures of molecular virus clones with known DRM and plasma samples of viremic HIV-1 infected individuals and we compared it to VirVarSeq, another virus variant detection tool exclusively working on Illumina deep sequencing data. MinVar was designed to be compatible with a diverse range of sequencing platforms and allows the detection of DRMs and insertions/deletions from deep sequencing data without the need to perform additional bioinformatics analysis, a prerequisite to a widespread implementation of HIV-1 genotyping using deep sequencing in routine diagnostic settings.

Abstract

Genotypic monitoring of drug-resistance mutations (DRMs) in HIV-1 infected individuals is strongly recommended to guide selection of the initial antiretroviral therapy (ART) and changes of drug regimens. Traditionally, mutations conferring drug resistance are detected by population sequencing of the reverse transcribed viral RNA encoding the HIV-1 enzymes target by ART, followed by manual analysis and interpretation of Sanger sequencing traces. This process is labor intensive, relies on subjective interpretation from the operator, and offers limited sensitivity as only mutations above 20% frequency can be reliably detected. Here we present MinVar, a pipeline for the analysis of deep sequencing data, which allows reliable and automated detection of DRMs down to 5%. We evaluated MinVar with data from amplicon sequencing of defined mixtures of molecular virus clones with known DRM and plasma samples of viremic HIV-1 infected individuals and we compared it to VirVarSeq, another virus variant detection tool exclusively working on Illumina deep sequencing data. MinVar was designed to be compatible with a diverse range of sequencing platforms and allows the detection of DRMs and insertions/deletions from deep sequencing data without the need to perform additional bioinformatics analysis, a prerequisite to a widespread implementation of HIV-1 genotyping using deep sequencing in routine diagnostic settings.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Medical Virology
04 Faculty of Medicine > University Hospital Zurich > Clinic for Infectious Diseases
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Uncontrolled Keywords:HIV-1; Genotyping; Deep sequencing; Bioinformatics; Drug resistance mutations; Minority variants
Language:English
Date:February 2017
Deposited On:25 Jan 2017 11:50
Last Modified:08 Dec 2017 22:35
Publisher:Elsevier
ISSN:0166-0934
Funders:Clinical Research Priority Program CRPP “Viral Infectious Diseases” by the University of Zurich
Publisher DOI:https://doi.org/10.1016/j.jviromet.2016.11.008
PubMed ID:27867045

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